The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning

• URL: http://arxiv.org/abs/2505.12904v1
• Date: Mon, 19 May 2025 09:37:46 GMT
• Title: The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning
• Authors: Hilde I. Hummel, Arwin Gansekoele, Sandjai Bhulai, Rob van der Mei,
• Abstract summary: Sound pollution in marine environments poses an increased threat to ocean health.<n>By monitoring this noise, the sources responsible for this pollution can be mapped.<n>This generates a large amount of data records, capturing a mix of sound sources such as ship activities and marine mammal vocalizations.
• Score: 0.7145837421668514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing level of sound pollution in marine environments poses an increased threat to ocean health, making it crucial to monitor underwater noise. By monitoring this noise, the sources responsible for this pollution can be mapped. Monitoring is performed by passively listening to these sounds. This generates a large amount of data records, capturing a mix of sound sources such as ship activities and marine mammal vocalizations. Although machine learning offers a promising solution for automatic sound classification, current state-of-the-art methods implement supervised learning. This requires a large amount of high-quality labeled data that is not publicly available. In contrast, a massive amount of lower-quality unlabeled data is publicly available, offering the opportunity to explore unsupervised learning techniques. This research explores this possibility by implementing an unsupervised Contrastive Learning approach. Here, a Conformer-based encoder is optimized by the so-called Variance-Invariance-Covariance Regularization loss function on these lower-quality unlabeled data and the translation to the labeled data is made. Through classification tasks involving recognizing ship types and marine mammal vocalizations, our method demonstrates to produce robust and generalized embeddings. This shows to potential of unsupervised methods for various automatic underwater acoustic analysis tasks.

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